English(EN) GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation

新方法应对遥感图像分割挑战

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-07 04:00

研究人员开发了两种新的遥感图像分割方法。GeoSelect将分割重新构建为空间程序的执行，从而能够精确控制航空影像中的空间、比较和序数关系。GeoSAM-Lite是一个轻量级基础模型，通过使用领域感知预训练策略和特征融合层，解决了在资源受限平台上部署大型模型的计算挑战。 AI

影响这些进展可以实现边缘设备上更高效、更准确的图像分析，并改善航空影像中复杂空间关系的处理。

排序理由两篇介绍遥感图像分割新方法的论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Yuhang Jiang, Guohui Deng, Miaozhong Xu, Chao Ruan, Jinling Zhao, Linsheng Huang · 2026-07-07 04:00

GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation

arXiv:2607.03869v1 Announce Type: cross Abstract: Referring remote sensing image segmentation isolates the object named by a natural-language expression in an aerial image. Existing training-free methods resolve the expression through implicit vision-language activations or regio…
arXiv cs.CV TIER_1 English(EN) · Yongcong Wang, Jie Zhang, Rui Jiang, Xubing Yang, Ting Yun, Li Zhang · 2026-07-07 04:00

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation

arXiv:2607.03760v1 Announce Type: new Abstract: The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sen…